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Abstract: Global change is impacting biodiversity across all habitats on earth. New selection pressures from changing climatic conditions and other anthropogenic activities are creating heterogeneous ecological and evolutionary responses across many species' geographic ranges. Yet we currently lack standardised and reproducible tools to effectively predict the resulting patterns in species vulnerability to declines or range changes. We developed an informatic toolbox that integrates ecological, environmental and genomic data and analyses (environmental dissimilarity, species distribution models, landscape connectivity, neutral and adaptive genetic diversity, genotype‐environment associations and genomic offset) to estimate population vulnerability. In our toolbox, functions and data structures are coded in a standardised way so that it is applicable to any species or geographic region where appropriate data are available, for example individual or population sampling and genomic datasets (e.g. RAD‐seq, ddRAD‐seq, whole genome sequencing data) representing environmental variation across the species geographic range. To demonstrate multi‐species applicability, we apply our toolbox to three georeferenced genomic datasets for co‐occurring East African spiny reed frogs (Afrixalus fornasini, A. delicatus and A. sylvaticus) to predict their population vulnerability, as well as demonstrating that range loss projections based on adaptive variation can be accurately reproduced from a previous study using data for two European bat species (Myotis escalerai and M. crypticus). Our framework sets the stage for large scale, multi‐species genomic datasets to be leveraged in a novel climate change vulnerability framework to quantify intraspecific differences in genetic diversity, local adaptation, range shifts and population vulnerability based on exposure, sensitivity and landscape barriers.

Background: Due to the ageing population and evolving lifestyles occurring in China, middle-aged and elderly populations have become high-risk groups for cardiovascular disease (CVD). The aim of this study was to analyse the incidence characteristics of CVD in these populations and develop a prediction model by using data from the China Health and Retirement Longitudinal Study (CHARLS). Methods: We used follow-up data from the CHARLS to analyse CVD incidence in the Chinese middle-aged and elderly population over a time span of 9 years. Five machine learning (ML) algorithms were employed for risk prediction. Data preprocessing included missing value imputation via random forest. Feature selection was performed using the Least Absolute Shrinkage and Selection Operator (Lasso CV) method with cross-validation prior to model training. The application of the synthetic minority over-sampling technique (SMOTE) to address class imbalance. Model performance was evaluated via analyses including the area under the ROC curve (AUC), precision, recall, F1 score, and SHAP plots for interpretability. Results: In accordance with the exclusion criteria, 12,580, 12,061, 11,545, and 11,619 participants were enrolled in four follow-up rounds. The cumulative incidence (CI) of CVD at 2, 4, 7, and 9 years was 2.846%, 8.971%, 17.869% and 20.518%,, respectively. Significant differences in CVD incidence were observed across gender, age, ethnicity, and region, with higher rates observed in females and in the northeast region. Ultimately, 8,080 participants and 24 features were analysed for CVD risk prediction. Five ML models were built based on these features. Although the LGB model achieves an AUC of 0.818, indicating strong overall performance, its F1 score and recall rate are relatively low, at 0.509 and 43.1%, respectively. Shapley additive explanations (SHAP) analyses revealed the importance of key features, such as night sleep duration, TG levels, and waist circumference, in predicting outcomes, and highlighted the nonlinear relationships between these features and CVD risk. Conclusions: Gender, age, ethnicity, and region are significant factors influencing CVD incidence. Although the LGB model demonstrates good overall performance, its low F1 score and recall rate reveal limitations in identifying high-risk cardiovascular disease patients. [ABSTRACT FROM AUTHOR]

Pasteurization is a necessary process that has to be done in order to ensure the health of the general public and for consumption dates (perished goods). The employment of artificial intelligence in pasteurization changes the course of technology and embodies a radical leap forward in food safety and quality. Artificial intelligence integrates precision, control, and efficiency levels of pasteurization mainstreamed traditionally by conventional food processing methods. Several artificial intelligence applications have been implemented for microbiological safety assessment, predictive modelling, and optimization of pasteurization processes that aim to reduce failure costs due to recall incidents and the necessity for costly quality assurance control measures. By using AI to process, industries can customize the pasteurization temperature and duration of heat in order to sterilize harmful bacteria and pathogens while preserving nutrition. AI also means predictive modelling and in the long run that will help dramatically decrease energy by curbing food waste which affects operational costs and sustainability. The present paper aims to provide an overview of artificial intelligence concerning food pasteurization in terms of process optimization, monitoring and control, microbiological safety assessment, and predictive modelling. [ABSTRACT FROM AUTHOR]

Retrofitting existing buildings is a cornerstone of Europe's strategy for a sustainable built environment. Therefore, accurate short-term forecasts to evaluate policy impacts and inform future strategies are needed. This study investigates the short-term predictive modelling of renovation activity in Belgium, focusing on overall renovation activity (RA) and energy-specific renovation activity (EA). Using data from 2012 to 2023, linear modelling was employed to analyze the relationships between RA/EA and macroeconomic indicators, market confidence, building permits, and loan data, with model performance evaluated using Mean Absolute Percentage Error (MAPE). Monthly data and time lags of up to 24 months were considered. The three best-performing models for RA achieved MAPE values between 2.9% and 3.1%, with validated errors ranging from 0.1% to 4.1%. For EA, the best models yielded MAPE values between 4.4% and 4.6% and validated errors between 8.9% and 14%. Renovation loans and building permits emerged as strong predictors for RA, while building material prices and loans were more relevant for EA. The time lag analysis highlighted that renovation processes typically span 15–24 months following loan approval. However, the low accuracy observed for EA underscores the need for further refinement. This explorative effort forms a solid base, inviting additional research to enhance our predictive capabilities and improve short-term modelling of the (green) residential renovation market. [ABSTRACT FROM AUTHOR]

Background: Event medicine is a field of medicine that encompasses the provision of healthcare to spectators/attendees at sports stadiums, music events, and festivals. This article explores existing theory to understand the evidence base currently afforded to operational practice. Aim: The study aim is to explore the current literature on event medicine to identify collective themes and areas for future research, prompt clinician reflective practice, and inform future standards of professional practice. Methods: A six-stage thematic-analysis-based literature review was conducted. The electronic databases of Google Scholar, Medline and PubMed were searched between January and April 2024. The search terms used were 'event medicine', 'mass gathering medicine', stadium medicine', and crowd medicine'. Articles prior to January 2004 were excluded. The search included English language full-text articles. Findings: A total of 32 articles were selected. They originated from Europe, Northern America, Southern Africa, and Asia, across a variety of sporting, outdoor festival, and music events. From their analysis, five main themes were identified: patient presentations; medical resource skill mix; predictive modelling; transfer to hospital rates; and acute cardiac events. Conclusion: Event medicine operational practice is under-researched, and essentially affords a large theory-practice gap in event medical service planning, provision, and application. Event-medicine clinicians should aim to deliver prehospital medical care that reflects the complexity of the five identified themes, as well as critically analyse existing event medical data, challenge their conceptual roles, and seek to develop future improved core standards of event medicine with a view to developing safer models of care for event spectators/attendees. [ABSTRACT FROM AUTHOR]

The need for an efficient adaptation of existing polypropylene (PP) formulations or the creation of new formulations has become increasingly important in various industries. Variations in viscosity resulting from changes in raw materials, fillers, and additives can have a significant impact on the processing and quality of PP products. This study presents the development of an analytical model designed to predict the shear viscosity of complex PP blends. By integrating established mixing rules with novel fitting parameters, the model provides a systematic and efficient method for managing variability in PP formulations. Experimental data from binary and multi-component blends were used to validate the model, demonstrating high prediction accuracy over a range of shear rates. The proposed model serves as a valuable tool for compounders and manufacturers to optimise PP formulations and develop new recipes with consistent processing and product quality. Future work will include industrial-scale trials and further evaluation against advanced machine learning approaches. [ABSTRACT FROM AUTHOR]

Through the increasing use of supplementary cementitious materials, the properties of concrete have taken on increased significance in a design code. Using reliable prediction models based on a small data set for the mechanical properties and durability of concrete can reduce the number of trial batches and experiments needed to produce useful design data in the laboratory, reducing time as well as resources. In this study, we investigate how the properties of water penetration, chlorine resistance, and compressive strength can be predicted by polynomial regression (PR), random forest (RF) regression, and artificial neural networks (ANNs) based on the input values of density, workability, and the constituent amount of rice husk ash, cement, and nano SiO 2 . We vary the training data used and test the coefficient of determination ( R 2 score) on the remaining data as a test set to measure predictive capability. We show that RFs and ANNs outperform PR in all settings and have unambiguously extrapolating properties when hyperparameter optimization is designed for this purpose. Remarkably, we obtain R 2 scores on the test data of 0.858 − 0.990 for RFs and 0.825 − 0.985 for ANNs. [ABSTRACT FROM AUTHOR]

The Inner Mongolia section of the Yellow River is a seasonal frozen soil area, where the freeze–thaw effect can alter soil strength and compressibility, affecting bank stability. This study takes the banks sandy silt of the Inner Mongolia section of the Yellow River as the research object. It systematically investigates the relationship between shear strength parameters and compression index of sandy silt and the initial dry density, water content, and freeze–thaw cycles of the soil. It analyzes the order and significance of influencing factors, establishes prediction models of shear strength and compression index, and evaluates the effects of freeze–thaw cycles on soil cohesion and shear strength. The results show that the shear strength index of sandy silt is proportional to changes in initial dry density and inversely proportional to changes in water content. After 10 freeze–thaw cycles, the cohesion of the soil decreases by 22.53 to 58.85%, and the shear strength decreases by 22.67 to 58.91%. The internal friction angle is less affected by freeze–thaw and tends to be stable overall. The smaller the initial dry density and the greater the water content, the greater the compression index and compressibility of the soil, but freeze–thaw has little effect on compression index. The factors affecting sandy silt shear strength and compression index are ranked as dry density > moisture content > freeze–thaw cycles. The stepwise regression model of soil shear strength and compression index based on initial dry density, water content, and freeze–thaw cycles is effective, providing technical guidance for engineering practice. [ABSTRACT FROM AUTHOR]

Enhancing the precision of supply chain management and reducing operational costs are crucial for the development of the cross-border e-commerce market. However, existing research often overlooks the demand uncertainty caused by seasonal variations and the challenges of handling returns in logistics. Therefore, this paper proposes a SARIMA-CNN-BiLSTM prediction model that effectively captures both the seasonal and nonlinear characteristics of cross-border e-commerce supply chains. Additionally, by incorporating the returns process, a supply chain distribution optimization model is developed with the objective of minimizing total operational costs. The model is solved using an improved whale optimization algorithm. In validation with real-world data, the SARIMA-CNN-BiLSTM model achieved a mean absolute percentage error reduction of 6.479 and 7.703 compared to convolutional neural network (CNN) and BiLSTM models, respectively. Moreover, the chosen optimization algorithm reduced the cost by 231,310 CNY, 62,564 CNY, and 131,632 CNY compared to the whale optimization algorithm, genetic algorithm, and particle swarm optimization, respectively. The proposed approach provides robust support for cross-border e-commerce enterprises in reducing costs and enhancing efficiency in their operations. [ABSTRACT FROM AUTHOR]

Non-alcoholic fatty liver disease (NAFLD) is a chronic medical ailment characterized by accumulation of excessive fat in the liver of non-alcoholic patients. In absence of any early visible indications, application of machine learning based predictive techniques for early prediction of NAFLD are quite beneficial. The objective of this paper is to present a complete framework for guided development of varied predictive machine learning models and predict NAFLD disease with high accuracy. The framework employs step-by-step data quality enhancement to medical data such as cleaning, normalization, data upscaling using SMOTE (for handling class imbalances) and correlation analysis-based feature selection to predict NAFLD with high accuracy using only clinically recorded identifiers. Comprehensive comparative analysis of prediction results of seven machine learning predictive models is done using unprocessed as well as quality enhanced data. As per the observed results, XGBoost, random forest and neural network machine learning models reported significantly higher accuracies with improved 'AUC' and 'ROC' values using preprocessed data in contrast to unprocessed data. The prediction results are also assessed on various quality metrics such as 'accuracy', 'f1-score', 'precision', and 'recall' significantly support the need for presented methodologies for qualitative NAFLD prediction modelling. [ABSTRACT FROM AUTHOR]

This study explores the development and application of machine learning (ML) metamodels for the thermo-mechanical analysis of Friction Stir Welding (FSW). The main objective is to address the challenge of accurately predicting the thermo-mechanical behaviour of materials in FSW processes. Using finite element models, a high-fidelity dataset consisting of 20 Hammersley design datapoints is generated which is then used to develop a low-fidelity dataset of 420 datapoints using KNN (K-Nearest Neighbor) imputation. This low-fidelity dataset is used to train and test nine different ML metamodels (namely Linear Regression, Random Forest (RF), Support Vector Machines (SVM), AdaBoost, Gaussian Process, Gradient Boosting, Decision Tree, Histogram-based Gradient Boosting and Extreme Gradient Boosting). The performance of these metamodels is evaluated based on various metrics like R 2 (Coefficient of Determination), MAE (Mean Absolute Error) and MSE (Mean Squared Error). The findings reveal significant variance in the metamodels' performance. Notably, Decision Tree, Gradient Boosting, XGB (Extreme Gradient Boosting) and Random Forest metamodels are found to be the top four performers. [ABSTRACT FROM AUTHOR]

With the rapid development of industrial automation and intelligence, the consumption of resources and the environmental impact of production processes cannot today be ignored. Today, natural gas, as a commonly used energy source, produces significantly lower emissions of carbon dioxide, sulphur dioxide, and nitrogen oxides from combustion than coal and oil, and can be further purified to remove the small amount of impurities it contains, such as sulphur compounds. Therefore, purified natural gas (hereinafter referred to as purified gas), as a clean energy source, plays an important role in realising sustainable development. At the same time, It becomes more and more important to dispatch purified gas resources reasonably and accurately, and the paramount factor is that the load of purified gas needs to be predicted accurately. Therefore, this paper proposes a Transformer–GRU-based hourly prediction model for purified gas. The model uses the Transformer model for data fusion and feature extraction, and then combines the time series processing capability of the Gate Recurrent Unit (GRU) model to capture long-term dependencies and short-term dynamic changes in time series data. In this paper, the purified gas load data of Chongqing Municipality in 2020 was first preprocessed, and then divided into daily and hourly load datasets according to the measurement step. Meanwhile, considering the influence of temperature factor, the experimental dataset is subdivided according to whether it includes temperature data or not, and then the Transformer–GRU model was built for prediction, respectively. The results show that, compared with the Dual-Stage Attention-Based Recurrent Neural Network (DA-RNN) and the Transformer and GRU models alone, the Transformer–GRU model exhibits good performance in terms of the coefficient of determination, the average absolute percentage error, and mean square error, which can well meet the requirement of hourly prediction accuracy and has greater application value. [ABSTRACT FROM AUTHOR]

This research focuses on developing the optimal machine learning (ML) based predictive model for calculating the factor of safety (FSMP) for finite slopes using the Morgenstern-Price method of slices. The ML models utilize geometric and geotechnical parameters, including slope angle, friction angle, cohesion, slope height, unit weight, horizontal seismic acceleration coefficient, and the ratio of horizontal to vertical seismic acceleration coefficients. A comprehensive dataset of 19,128 data points is generated using in-house MATLAB code. These data points are trained with the ML models to learn the underlying correlations for the prediction of FSMP. Various ML predictive models, such as multiple linear regression, support vector regression, Gaussian process regression, random forest, extreme gradient boosting, and artificial neural networks, are considered for constructing the optimal model. The objective is to develop a tailored framework for arriving at the best-performing predictive model for replication of pseudo-static stability analysis of soil slopes in geotechnical engineering. Comparison of different data-driven models are also presented. The study also utilized interpretable machine learning models with Shapley values to mitigate the inherent "black box" nature of ML models. The study also establishes a physically interpretable error validation model to assess model predictions. The findings illustrate the effectiveness and precision of the Gaussian process regression (GPR) model, as evidenced by R2 error metric values of 99.9% and 99.8% for the training and test sets, respectively. Further, the error metric for the artificial neural network (ANN) achieved values of 99.7% and 99.6% for the training and test sets, respectively. The GPR model offers conservative results over ANN, making it the preferred predictive model for safe FSMP predictions. It serves as an efficient estimation tool for field practitioners, can be integrated into smartphones and above all integrated into the performance function for uncertainty quantification in the otherwise computationally expensive Monte Carlo simulations. Design charts are also generated using the selected optimal model for depicting the generalizability of this model, enabling geotechnical engineers to determine FSMP without complex calculations. This research showcases the potential of ML techniques for complex geotechnical problems, advancing conventional slope stability analysis and opening avenues for their practical and reliable use in geotechnical engineering. [ABSTRACT FROM AUTHOR]

Cervical cancer represents a significant public health challenge, particularly affecting women's health globally. This study aims to advance the understanding of cervical cancer risk prediction research through a bibliometric analysis. The study identified 800 records from Scopus and Web of Science databases, which were reduced to 142 unique records after removing duplicates. Out of 100 abstracts assessed, 42 were excluded based on specific criteria, resulting in 58 studies included in the bibliometric review. Multiple scoping methods such as thematic analysis, citation analysis, bibliographic coupling, natural language processing, Latent Dirichlet Allocation and other visualisation techniques were used to analyse related publications between 2013 and 2024. The key findings revealed the importance of interdisciplinary collaboration in cervical cancer risk prediction, integrating expertise from mathematical disciplines, biomedical health, healthcare practitioners, public health, and policy. This approach significantly enhanced the accuracy and efficiency of cervical cancer detection and predictive modelling by adopting advanced machine learning algorithms, such as random forests and support vector machines. The main challenges were the lack of external validation on independent datasets and the need to address model interpretability to ensure healthcare providers understand and trust the predictive models. The study revealed the importance of interdisciplinary collaboration in cervical cancer risk prediction. It made recommendations for future research to focus on increasing the external validation of models, improving model interpretability, and promoting global research collaborations to enhance the comprehensiveness and applicability of cervical cancer risk prediction models. [ABSTRACT FROM AUTHOR]

We lack evidence on the cumulative effectiveness of machine learning (ML)-driven interventions in perioperative settings. Therefore, we conducted a systematic review to appraise the evidence on the impact of ML-driven interventions on perioperative outcomes. Ovid MEDLINE, CINAHL, Embase, Scopus, PubMed, and ClinicalTrials.gov were searched to identify randomised controlled trials (RCTs) evaluating the effectiveness of ML-driven interventions in surgical inpatient populations. The review was registered with PROSPERO (CRD42023433163) and conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Meta-analysis was conducted for outcomes with two or more studies using a random-effects model, and vote counting was conducted for other outcomes. Among 13 included RCTs, three types of ML-driven interventions were evaluated: Hypotension Prediction Index (HPI) (n =5), Nociception Level Index (NoL) (n =7), and a scheduling system (n =1). Compared with the standard care, HPI led to a significant decrease in absolute hypotension (n =421, P =0.003, I2=75%) and relative hypotension (n =208, P <0.0001, I2=0%); NoL led to significantly lower mean pain scores in the post-anaesthesia care unit (PACU) (n =191, P =0.004, I2=19%). NoL showed no significant impact on intraoperative opioid consumption (n =339, P =0.31, I2=92%) or PACU opioid consumption (n =339, P =0.11, I2=0%). No significant difference in hospital length of stay (n =361, P =0.81, I2=0%) and PACU stay (n =267, P =0.44, I2=0) was found between HPI and NoL. HPI decreased the duration of intraoperative hypotension, and NoL decreased postoperative pain scores, but no significant impact on other clinical outcomes was found. We highlight the need to address both methodological and clinical practice gaps to ensure the successful future implementation of ML-driven interventions. CRD42023433163 (PROSPERO). [ABSTRACT FROM AUTHOR]

Diapause is a vital survival strategy for insects, enabling them to conserve energy and endure adverse conditions. Understanding how diapause affects insect phenology and population dynamics is crucial for the effective management of insect pests. Predictive pest phenological models can be invaluable tools for providing essential information to support management strategies. This study presents a modelling framework to incorporate diapause into phenological models when biological information on variables regulating and functions describing diapause induction and termination are lacking or limited. In our framework, insect phenology is divided into a set of phases characterized by specific events (diapause induction and termination) and processes (development of diapausing and post‐diapausing biological stages). The phenology is simulated by a stage‐structured model based on the Kolmogorov equation, and the temperature‐dependent development rate functions are described by the Brière functional form. Our modelling framework was tested on a case study involving the prediction of the phenology of the codling moth, (Cydia pomonella L. 1758). Model calibration and validation were performed using four time‐series adult trap catch data collected in the Emilia Romagna Region from 2021 to 2023. The calibration procedure allowed obtaining realistic parameters related to the temperature threshold triggering diapause termination and the development rate function of post‐diapausing larvae and pupae. Model validation proved successful in simulating both the initial emergence and the overall phenological patterns of adults across the three observed generations. The methodological framework proposed here aims to facilitate the introduction of diapause in phenological models improving also their predictive abilities. The model may serve as an accurate and knowledge‐based tool for planning and implementing pest monitoring and control actions based on the realistic predictions provided by the model on the phenological status of the pest. [ABSTRACT FROM AUTHOR]

Coral reef ecosystems support high fish biodiversity through ecological interactions with structural complexity across multiple spatial scales including coral colony architecture and the surrounding seascape structure. In an era where the complexity of coral reef ecosystems is being diminished, understanding the importance of structural characteristics beyond single focal patches has the potential to better inform actions for protecting, restoring or creating habitat for reef‐associated species. A seascape ecology approach was applied to explore the associations between multiple scales of seascape structure and fish assemblage response variables within a small (49.6 km2) offshore no‐take MPA, Sir Bu Nair Island Protected Area, in Sharjah, United Arab Emirates. Fish–seascape associations were modelled with single regression trees. Both in situ and remote sensing–derived variables produced the best models with highest contributions from coral cover, amount of hard‐bottom habitat type and structural complexity of the seafloor terrain. Fish species richness was significantly higher where coral cover exceeded 35%. The hard‐bottom areas with coral supported diverse assemblages dominated by carnivorous and omnivorous fishes. The Sir Bu Nair Island Protected Area provides a critical refuge for threatened and regionally overexploited species including those with low resilience to fishing. The ecological success of this protected area is key to safeguarding regional marine biodiversity and recovering fish populations to enhance food security. [ABSTRACT FROM AUTHOR]

Aim: To evaluate if, and to what extent, machine learning models can capture clinically defined Stage III/IV periodontitis from self‐report questionnaires and demographic data. Materials and Methods: Self‐reported measures of periodontitis, demographic data and clinically established Stage III/IV periodontitis status were extracted from two Danish population‐based cohorts (The Copenhagen Aging and Midlife Biobank [CAMB] and The Danish Health Examination Survey [DANHES]) and used to develop cross‐validated machine learning models for the prediction of clinically established Stage III/IV periodontitis. Models were trained using 10‐fold cross‐validations repeated three times on the CAMB dataset (n = 1476), and the resulting models were validated in the DANHES dataset (n = 3585). Results: The prevalence of Stage III/IV periodontitis was 23.2% (n = 342) in the CAMB dataset and 9.3% (n = 335) in the DANHES dataset. For the prediction of clinically established Stage III/IV periodontitis in the CAMB cohort, models reached area under the receiver operating characteristics (AUROCs) of 0.67–0.69, sensitivities of 0.58–0.64 and specificities of 0.71–0.80. In the DANHES cohort, models derived from the CAMB cohort achieved AUROCs of 0.64–0.70, sensitivities of 0.44–0.63 and specificities of 0.75–0.84. Conclusions: Applying cross‐validated machine learning algorithms to demographic data and self‐reported measures of periodontitis resulted in models with modest capabilities for the prediction of Stage III/IV periodontitis in two Danish cohorts. [ABSTRACT FROM AUTHOR]

Objectives: The burgeoning prevalence of cardiometabolic disorders, including type 2 diabetes mellitus (T2DM) and metabolic syndrome (MetS) within Africa is concerning. Machine learning (ML) techniques offer a unique opportunity to leverage data-driven insights and construct predictive models for MetS risk, thereby enhancing the implementation of personalised prevention strategies. In this work, we employed ML techniques to develop predictive models for pre-MetS and MetS among diabetic patients. Methods: This multi-centre cross-sectional study comprised of 919 T2DM patients. Age, gender, novel anthropometric indices along with biochemical measures were analysed using BORUTA feature selection and an ensemble majority voting classification model, which included logistic regression, k-nearest neighbour, Gaussian Naive Bayes, Gradient boosting classification, and support vector machine. Results: Distinct metabolic profiles and phenotype clusters were associated with MetS progression. The BORUTA algorithm identified 10 and 16 significant features for pre-MetS and MetS prediction, respectively. For pre-MetS, the top-ranked features were lipid accumulation product (LAP), triglyceride-glucose index adjusted for waist-to-height ratio (TyG-WHtR), coronary risk (CR), visceral adiposity index (VAI) and abdominal volume index (AVI). For MetS prediction, the most influential features were VAI, LAP, waist triglyceride index (WTI), Very low-density cholesterol (VLDLC) and TyG-WHtR. Majority voting ensemble classifier demonstrated superior performance in predicting pre-MetS (AUC = 0.79) and MetS (AUC = 0.87). Conclusion: Identifying these risk factors reveals the complex interplay between visceral adiposity and metabolic dysregulation in African populations, enabling early detection and treatment. Ethical integration of ML algorithms in clinical decision-making can streamline identification of high-risk individuals, optimize resource allocation, and enable precise, tailored interventions. [ABSTRACT FROM AUTHOR]

The heat generated during metal cutting processes like end milling results in to higher temperatures which affects the quality of the surface produced. So, in order to get better surface quality, it is important to reduce the cutting zone temperatures. In the materials having high thermal conductivity the internal temperature rise of the workpiece also needs to be controlled. In this work, the study of the effects of controllable process parameters of CNC end milling on workpiece surface temperature rise, workpiece internal temperature rise and surface roughness during machining of aluminium alloy (Al 7075) was undertaken. The input parameters were spindle speed, feed rate, axial depth of cut, radial depth of cut and tool helix angle. The inclusion of helix angle as an input factor and, simultaneous reduction of workpiece surface temperature rise and workpiece internal temperature rise without compromising on the quality of the product were the major highlights of this work. This work also evaluates the trade-offs between heat generation and cutting quality. The central composite rotatable design was used for planning of the experiments and to develop predictive models. The direct and the interaction effects of the input parameters were studied and discussed. The optimization of process parameters was carried out by desirability analysis. The individual and composite desirability values were unity, which highlights, the successful implementation of the adopted methodology for achieving the desired goals. The optimum values of input parameters were: spindle speed: 4407 rpm, feed: 493 mm/min, axial depth of cut: 0.51 mm, radial depth of cut: 6.46 mm and helix angle: 30°. The results of the optimization were confirmed through the confirmation experiments with reasonable accuracy. [ABSTRACT FROM AUTHOR]

Continuous growth of the manufacturing sector is resulting in to higher energy demand due to which the manufacturing costs and greenhouse gas emissions are also increasing. Beside reduction in energy consumption; improvement in energy efficiency, power factor and reduction in cutting temperatures are also vital to ensure better sustainability of the machining sector. This work evaluates the trade-offs between energy, heat generation and cutting quality during milling of medium carbon steel (EN8) alloy steel. The effects of input process parameters viz. spindle speed, feed rate, axial depth of cut, radial depth of cut and tool helix angle has been studied on the energy consumption, energy efficiency, power factor, cutting temperatures, surface roughness response parameters. The inclusion of helix angle as an input factor and, using energy efficiency and power factor as output parameters are the major highlights of this work. The machining experiments were conducted using response surface methodology for design of experiments. The multi objective optimization was carried out by using desirability approach, for three different groups of response variables considering the different importance of energy consumption, cutting temperatures and surface roughness, under different manufacturing circumstances. The predictability of the multiple regression approach was found to be more than 90% for all the responses which highlights model significance. The direct and interaction effect were studied and discussed in details for all the responses. The values of the composite desirability achieved in all the three types of optimization problems were on higher side (0.813, 1 and 0.794). The results of the optimization were confirmed by conducting the experiments the optimized settings. The percentage error between experimental and RSM predicted result was found to be within acceptable limits. This study can be helpful for reducing the energy consumption and cutting temperature without compromising on surface roughness, in the machining of medium carbon steel. [ABSTRACT FROM AUTHOR]

This study aimed to enhance the efficiency of by-product raw materials from the sugar industry for use as fuel. The approach involved developing an equation to calculate the higher heating value (HHV) for each type of raw material using a regression method. Additionally, a simplex-centroid mixture design (SCMD) was employed to estimate the lower heating value (LHV) based on the mixing ratios by weight of sugarcane trash leaves (SCL), sugarcane bagasse (SCB), and filter cake (FTC). The results demonstrated that the developed model accurately estimated the HHV for each raw material. The ultimate analysis showed high statistical appropriateness, with an R2 of 0.83. The standard error of estimation was 0.74 MJ/kg, and the mean absolute error was 0.76%. Furthermore, the SCMD effectively estimated the LHV of the SCL, SCB, and FTC mixture ratios, achieving an R2 of 99.77%. The evaluation and validation of the prediction equation revealed a mean absolute error of 7.57% and a mean bias error of 6.31%. The findings of this study can be used to enhance the combustion efficiency of sugar industry by-products for use as fuel by selecting the optimal mixing ratio for each type of raw material. [ABSTRACT FROM AUTHOR]

This study investigates the application of Deep Neural Networks (DNNs) to optimize the formulation, development, and performance evaluation of Metformin Hydrochloride sustained-release tablets, a key medication for managing Type 2 diabetes. Traditional drug formulation methods are often time-consuming and constrained by the complexity of the formulation process. This research addresses these challenges by utilizing DNNs to create predictive models that accurately forecast critical formulation outcomes, such as dissolution rates. The study began with a comprehensive pre-formulation analysis to assess the physicochemical properties of Metformin Hydrochloride and its compatibility with various excipients. Using a 3² factorial Design of Experiments (DoE) approach, 24 formulations (F1-F24) were prepared through the wet granulation method, varying the concentrations of Polyox WSR 303 and Povidone K30. The tablets were evaluated for postcompression parameters and in vitro dissolution performance. Experimental data from these formulations were used to train a DNN model to predict optimal formulation parameters based on performance metrics. Among the formulations, the DNN identified F1 as the optimal formulation, predicting a drug release of 99.23%. Experimental validation of F1 revealed an in vitro drug release of 98.95%, closely matching the predicted value. The optimal composition included 95 mg of Polyox WSR 303 and 115 mg of Povidone K30. A comparison with a computerized simulation model showed a difference factor (f1) of 1.71 and a similarity factor (f2) of 91.48, confirming a high degree of similarity between the dissolution profiles. This study highlights the potential of deep learning to streamline pharmaceutical development, improve formulation precision. [ABSTRACT FROM AUTHOR]

In the evolving area of immunoinformatics, accurate prediction of B-cell epitopes is vital for vaccine improvement and healing interventions. This study offers a novel predictive pipeline that employs a Support Vector Machine (SVM) model optimized by way of Genetic Algorithms (GA) to decorate the accuracy and reliability of B-cellular epitope predictions. By systematically extracting key capabilities, inclusive of β-turns, antigenicity, and hydrophobicity, from peptide and protein sequences, this study applied a robust statistics preprocessing approach that consists of labeling, normalization, and dataset splitting. The performance of the proposed SVM model is carefully evaluated towards traditional methods, including Random Forest (RF) and K-Nearest Neighbors (KNN). The proposed SVM model completed an accuracy of 92.5%, a precision of 89.3%, a bear-in-mind of 91.0%, and an F1 rating of 90.1%. In comparison, the RF model obtained an accuracy of 85.0%, at the same time as the KNN version reached an accuracy of 82.5%. Visualizations, together with function importance plots, ROC curves, and confusion matrices, illustrate the model's advanced performance and its capacity for real-international packages. This study's findings underscore the importance of integrating superior machine learning strategies in immunological research and offer a complete framework for future research in epitope prediction. [ABSTRACT FROM AUTHOR]

This study aimed to enhance the efficiency of by-product raw materials from the sugar industry for use as fuel. The approach involved developing an equation to calculate the higher heating value (HHV) for each type of raw material using a regression method. Additionally, a simplex-centroid mixture design (SCMD) was employed to estimate the lower heating value (LHV) based on the mixing ratios by weight of sugarcane trash leaves (SCL), sugarcane bagasse (SCB), and filter cake (FTC). The results demonstrated that the developed model accurately estimated the HHV for each raw material. The ultimate analysis showed high statistical appropriateness, with an R2 of 0.83. The standard error of estimation was 0.74 MJ/kg, and the mean absolute error was 0.76%. Furthermore, the SCMD effectively estimated the LHV of the SCL, SCB, and FTC mixture ratios, achieving an R2 of 99.77%. The evaluation and validation of the prediction equation revealed a mean absolute error of 7.57% and a mean bias error of 6.31%. The findings of this study can be used to enhance the combustion efficiency of sugar industry by-products for use as fuel by selecting the optimal mixing ratio for each type of raw material.

Data has become a high-value commodity in manufacturing. There is a growing realisation that the data-driven applications could become strong differentiators of manufacturing enterprises. To guide the developments in digitisation, a widely accepted framework is needed. In the absence of the universal framework, the components making a digital enterprise are captured in an example framework that is introduced in the paper. The adoption of new technology and software solutions has increased complexity of manufacturing systems. In addition, new product introductions have become more frequent and the demand more variable. A digital space enables optimisation and simulation of decisions before their realisation in the physical space. Predictive modelling with its time dimension is a valuable actor in the digital space. Three challenges of predictive modelling such as model complexity, model interpretability, and model reuse are identified in this paper. The coverage of each challenge in the literature is illustrated with the recently published papers. The main aspects of these challenges and the synthesis of the developments in digital manufacturing are articulated in the form of eight observations that could guide the future research. [ABSTRACT FROM AUTHOR]

Abstract Objectives To investigate the feasibility and effectiveness of a deep learning (DL) super-resolution (SR) ultrasound image reconstruction model for predicting cervical lymph node status in patients with papillary thyroid carcinoma(PTC). Methods In this retrospective study, researchers recruited 544 patients with PTC and randomly assigned them to training and test sets. SR ultrasound images were acquired using SR technology to improve image resolution, and artificial features and DL features were extracted from the original (OR) and SR images, respectively, to construct a ML, DL model. The best model was selected and aggregated with clinical parameters to construct the nomogram. The performance of the model is evaluated by ROC curves, calibration curves and decision curves. Results In distinguishing the presence or absence of metastatic lymph nodes, the predictive performance of the SR_ResNet 101 and SR_SVM models based on SR outperformed those based on OR. In the test set, SR_SVM AUC was 0.878 (95% CI 0.8203–0.9358), accuracy 0.854, while OR_SVM AUC was 0.822 (95% CI 0.7500–0.8937), accuracy 0.665. SR_ResNet 101 AUC was 0.799 (95% CI 0.7175–0.8806), accuracy 0.793, and OR_ResNet101 AUC was 0.751 (95% CI 0.6620–0.8401), accuracy 0.713. Subsequently, Nomogram_A and Nomogram_B were constructed by integrating the SR_SVM model and SR_ResNet 101 model, respectively, with clinical parameters, while Nomogram_C was constructed solely based on clinical indicators. In the test set, Nomogram_A demonstrated the best performance with an AUC of 0.930 (95% CI 0.8913–0.9682) and accuracy was 0.829. Nomogram_B AUC 0.868 (95% CI 0.8102–0.9261) and accuracy was 0.829, while Nomogram_C AUC 0.880 (95% CI 0.8257–0.9349) and accuracy was 0.787. The DeLong test revealed that the diagnostic performance of Nomogram_A based on SR_SVM was significantly higher than that of Nomogram_B, Nomogram_C, and the level of Radiologist (P